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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, AvgPool1d
from torch.nn.utils import weight_norm, spectral_norm
from torch import nn
from modules.vocoder_blocks import *
LRELU_SLOPE = 0.1
class DiscriminatorS(nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiScaleDiscriminator(nn.Module):
def __init__(self, cfg):
super(MultiScaleDiscriminator, self).__init__()
self.cfg = cfg
self.discriminators = nn.ModuleList(
[
DiscriminatorS(use_spectral_norm=True),
DiscriminatorS(),
DiscriminatorS(),
]
)
self.meanpools = nn.ModuleList(
[AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]
)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
if i != 0:
y = self.meanpools[i - 1](y)
y_hat = self.meanpools[i - 1](y_hat)
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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